An agent-based reinforcement learning approach to
improve human-robot-interaction in manufacturing
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Abstract
This work is aimed at the understanding and application of several emerging technologies as
they relate to improving the interactions which occur between robotic operators and their
human colleagues across a range of manufacturing processes. These interactions are
problematic, as variation in performance of human beings remains one of the largest sources
of disturbances within such systems, with potentially significant implications for productivity
if it continues unmitigated. The problem remains for the most part unaddressed, despite these
interactions becoming increasingly prevalent as the rate of adoption of automation
technologies increases.
By reconciling multiple areas encompassed by the wider domain of intelligent
manufacturing, the presented work identifies a methodology and a set of software tools which
leverage the strengths of neural-network-based reinforcement learning to develop intelligent
software agents capable of adaptable behaviour in response to observed environmental
changes. The methodology further focuses on developing representative simulation models
for these interactions following a pattern of generalisation, to effectively represent both
human and robotic elements, and facilitate implementation. By learning through their
interaction with the simulated manufacturing environment, these agents can determine an
appropriate policy, by which to autonomously adjust their operating parameters, as a
response to changes in their human colleagues. This adaptability is demonstrated to enable
the intelligent agents to determine an action policy which results in less observed idle time,
along with improved leanness and overall productivity, over multiple scenarios.
The findings of the work suggest that software agents that make use of a reinforcement
based learning approach are well suited to the task of enabling robotic adaptability in such a
way, and the developed methodology provides a platform for further development and
exploration, along with numerous insights into the effective development of these agents